AI on Academic vs Industry vs Competition
I cannot comment about the process for AI for academia. I can compare AI for competitions and AI for business. To clarify whatever I say is about ML not any other AI techniques. The process might be different for other techniques. But most of things that I say are general enough that I am assuming should still apply.

The main difference that I saw while doing ML for a competition vs. for a business was that of focus. 

When doing it for a competition for Kaggle the focus was mainly creating the model


machine learning metrics are specified for you
some data was given to you
business problem was given to you


When doing it for business what is different


given a business problem finding the parts that can actually benefit from ML. You have to define the ML problem in it and define how it actually benefits the business. This may involve significant discussions with business stakeholders, weighting the pros and cons of doing it versus doing something else, communicate the benefits to the business stakeholders, take them into confidence for the process to start
find the right data for the problem from scratch, ensure it is collected by rest of the system or brought from 3rd parties
define business metrics over and above machine learning metrics. At the end of day nobody really cares about whether ML model recall, accuracy is good or bad. What is important is the relevant business effect.
make the model, deploy it and integrate it with the rest of the system. This is important because if your goal is just to making the model you would not care about the factors associated with actually using it i.e. latency of predictions, cost of machines needed to run it etc.
A/B testing for the models, running multiple models in parallel, dynamically being able to adjust which models to use


Hope this gives some idea about the differences in AI for competitions and AI for business.